Breast and ovarian cancers harboring homologous recombination deficiencies (HRD) can benefit from platinum-based chemotherapies and PARP inhibitors. Standard diagnostic tests for detecting HRD utilize molecular profiling, which is not universally available especially for medically underserved populations. Here, we trained a deep learning approach for predicting genomically derived HRD scores from routinely sampled hematoxylin and eosin (H&E)-stained histopathological slides. For breast cancer, the approach was externally validated on three independent cohorts and allowed predicting patients' response to platinum treatment. Using transfer learning, we demonstrated the method's clinical applicability to H&E-images from high-grade ovarian tumors. Importantly, our deep learning approach outperformed existing genomic HRD biomarkers in predicting response to platinum-based therapies across multiple cohorts, providing a complementary approach for detecting HRD in patients across diverse socioeconomic groups.

LBA is a compensated consultant and has equity interest in io9, LLC and Genome Insight. His spouse is an employee of Biotheranostics, Inc. SML is a co-founder and has equity interest in io9, LLC. AA and LBA declare U.S. provisional patent application with serial numbers 63/366,392 for detecting homologous recombination deficiency from genomics data. ENB, SML, and LBA declare U.S. provisional patent application with serial numbers 63/269,033 for artificial intelligence architecture for predicting cancer biomarkers. All other authors declare they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This work was funded by a US National Institutes of Health grant R01ES032547 and UC San Diego start-up funding to LBA.

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The source data were openly available before the start of the study. All data were retrieved from the existing data sources.

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The collection of flash frozen and formalin-fixed paraffin-embedded (FFPE) slides from TCGA along with all clinical features were downloaded from the Genomic Data Commons (GDC; https://gdc.cancer.gov/). The collection of flash frozen slides from CPTAC were downloaded from The Cancer Imaging Archive (TCIA), and the genomics data was downloaded from the GDC. The collection of images from METABRIC and the associated SNP6 genotyping microarray data were downloaded from European Genome-Phenome Archive (EGA) with accession numbers: EGAD00010000270 and EGAD00010000266. The collection of samples from the metastatic breast cancer cohort are available on Sequence Read Archive (SRA) repository under BioProject accession number PRJNA793752.

https://gdc.cancer.gov/

https://ega-archive.org/datasets/EGAD00010000270

https://ega-archive.org/datasets/EGAD00010000266

https://www.ncbi.nlm.nih.gov/sra/SRX13584185